Eosinophil adhesion under flow conditions activates mechanosensitive signaling pathways in human endothelial cells
Bibliographic record
Abstract
Leukocyte transmigration can be affected by shear stress; however, the mechanisms by which shear stress modulates transmigration are unknown. We found that adhesion of eosinophils or an eosinophilic cell line to intereukin 4-stimulated endothelial cells led to a shear-dependent increase in endothelial cell intracellular calcium and increased phosphorylation of extracellular signal-regulated kinase (ERK) 2, but not c-Jun NH2-terminal kinase or p38 mitogen-activated protein kinase. Latex beads coated with antibodies were used to characterize the role of specific endothelial cell surface molecules in initiating signaling under shear conditions. We found that ligation of either vascular cell adhesion molecule-1 or E-selectin, but not major histocompatibility complex class I, induced a shear-dependent increase in ERK2 phosphorylation in cytokine-stimulated endothelial cells. Disassembly of the actin cytoskeleton with latrunculin A prevented ERK2 phosphorylation after adhesion under flow conditions, supporting a role for the cytoskeleton in mechano-sensing. Rapid phosphorylation of focal adhesion kinase and paxillin occurred under identical conditions, suggesting that focal adhesions were also involved in mechanotransduction. Finally, we found that Rho-associated protein kinase and calpain were both critical in the subsequent transendothelial migration of eosinophils under flow conditions. These data suggest that ligation of leukocyte adhesion molecules under flow conditions leads to mechanotransduction in endothelial cells, which can regulate subsequent leukocyte trafficking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".